Abstract: Radar signal detection and parameter estimation is central in passive surveillance systems, providing inputs for many information processing modules in order to detect, localize, indentify and intercept hostile targets. The proposed method for detecting radar signals and estimating their intra-pulse parameters in time-varying noisy environments consists of several stages: magnitude-squared envelopes calculation, adaptive noise floor estimation, detection statistics calculation, rising edge detection, time of arrival estimation, falling edge detection, time of departure estimation, pulse width estimation, amplitude estimation and center frequency and bandwidth estimation. Estimated intra-pulse parameters are wrapped into pulse descriptor words (PDWs) for information processing tasks, where each PDW consists of time of arrival, time of departure, pulse width, pulse amplitude, center frequency, signal bandwidth, noise floor level and additional useful information.

Abstract: In order to avoid various jamming attacks from intelligent jammers in modern complex wireless environments, a system and method is presented for a user radio to generate and implement an adaptive anti-jamming communication strategy. The said adaptive anti-jamming communication strategy is obtained via the training process for a specific neural network using Deep Double-Q Reinforcement learning algorithm in the strategy generation phase. The objective of this process is to discover a strategy to select the optimal radio action including transmission channel and transmission power for the user radio, which is changed adaptively to different jamming patterns to maximize the successful transmission rate (“jamming-free”) while retaining the power consumption of user radio as low as possible.

Abstract: Radar signal detection and parameter estimation is central in passive surveillance systems, providing inputs for many information processing modules in order to detect, localize, indentify and intercept hostile targets. The proposed method for detecting radar signals and estimating their intra-pulse parameters in time-varying noisy environments consists of several stages: magnitude-squared envelopes calculation, adaptive noise floor estimation, detection statistics calculation, rising edge detection, time of arrival estimation, falling edge detection, time of departure estimation, pulse width estimation, amplitude estimation and center frequency and bandwidth estimation. Estimated intra-pulse parameters are wrapped into pulse descriptor words (PDWs) for information processing tasks, where each PDW consists of time of arrival, time of departure, pulse width, pulse amplitude, center frequency, signal bandwidth, noise floor level and additional useful information.

Abstract: In order to avoid various jamming attacks from intelligent jammers in modern complex wireless environments, a system and method is presented for a user radio to generate and implement an adaptive anti-jamming communication strategy. The said adaptive anti-jamming communication strategy is obtained via the training process for a specific neural network using Deep Double-Q Reinforcement learning algorithm in the strategy generation phase. The objective of this process is to discover a strategy to select the optimal radio action including transmission channel and transmission power for the user radio, which is changed adaptively to different jamming patterns to maximize the successful transmission rate (“jamming-free”) while retaining the power consumption of user radio as low as possible.